The aim of this study was to optimize fluid bed granulation and tablets compression processes using design space approach. Type\r\nof diluent, binder concentration, temperature during mixing, granulation and drying, spray rate, and atomization pressure were\r\nrecognized as critical formulation and process parameters. They were varied in the first set of experiments in order to estimate\r\ntheir influences on critical quality attributes, that is, granules characteristics (size distribution, flowability, bulk density, tapped\r\ndensity, Carr�s index, Hausner�s ratio, and moisture content) using Plackett-Burman experimental design. Type of diluent and\r\natomization pressure were selected as the most important parameters. In the second set of experiments, design space for process\r\nparameters (atomization pressure and compression force) and its influence on tablets characteristics was developed. Percent of\r\nparacetamol released and tablets hardness were determined as critical quality attributes. Artificial neural networks (ANNs) were\r\napplied in order to determine design space. ANNs models showed that atomization pressure influences mostly on the dissolution\r\nprofile, whereas compression force affects mainly the tablets hardness. Based on the obtained ANNs models, it is possible to predict\r\ntablet hardness and paracetamol release profile for any combination of analyzed factors.
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